Bring deep SageMaker AI expertise to your coding agent with the AWS AI/ML skill in the Agent Toolkit for AWS
Your coding agent can select, train, optimize and deploy models on Amazon SageMaker AI while working across the rest of your AWS environment in a single natural-language workflow. The AWS AI/ML skill is now a core skill in the Agent Toolkit for AWS, so it works alongside the toolkit's other AWS skills like EC2, Bedrock, and more.
We released the new AWS AI/ML skill on the Agent Toolkit for AWS. The toolkit is now the single, supported integration point, replacing the previous AWS Labs skills and plugins, with a consistent and easy install and update path. The skill is also pre-installed into SageMaker Studio JupyterLab for use with Kiro or your coding agent of choice.
Moreover, we extended the SageMaker AI capability areas supported by skills.
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Inference optimization: optimize deployed models for latency, throughput, and cost
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Model customization: end-to-end guided fine-tuning workflows, from use case definition through training, evaluation and deployment
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Serverful training for both traditional ML and generative AI
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MLflow: experiment tracking and model lifecycle management
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JumpStart model deployment and model recommendations: deploy pre-built JumpStart models and get guided recommendations on the right model for the use case
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Processing jobs: data preparation and ETL on managed compute
The AWS AI/ML skill is available at no additional charge; you pay only for the AWS resources your agents use. To get started, install the skill from the Agent Toolkit for AWS on GitHub, or use it pre-installed in SageMaker Studio JupyterLab. To learn more about using a coding assistant with your SageMaker AI workflows, see the Amazon SageMaker AI documentation.